{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   year  month  day gender  births\n0  1969      1  1.0      F    4046\n1  1969      1  1.0      M    4440\n2  1969      1  2.0      F    4454\n3  1969      1  2.0      M    4548\n4  1969      1  3.0      F    4548",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>month</th>\n      <th>day</th>\n      <th>gender</th>\n      <th>births</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1969</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>F</td>\n      <td>4046</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1969</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>M</td>\n      <td>4440</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1969</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>F</td>\n      <td>4454</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1969</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>M</td>\n      <td>4548</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1969</td>\n      <td>1</td>\n      <td>3.0</td>\n      <td>F</td>\n      <td>4548</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 58
    }
   ],
   "source": [
    "births = pd.read_csv('births.csv')\n",
    "births.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "gender         F         M        All\nyear                                 \n1969     1753634   1846572    3600206\n1970     1819164   1918636    3737800\n1971     1736774   1826774    3563548\n1972     1592347   1673888    3266235\n1973     1533102   1613023    3146125\n1974     1543005   1627626    3170631\n1975     1535546   1618010    3153556\n1976     1547613   1628863    3176476\n1977     1623363   1708796    3332159\n1978     1626324   1711976    3338300\n1979     1705837   1793958    3499795\n1980     1762459   1855522    3617981\n1981     1772037   1863478    3635515\n1982     1797239   1888218    3685457\n1983     1775299   1867522    3642821\n1984     1791802   1881766    3673568\n1985     1834774   1930290    3765064\n1986     1833708   1926987    3760695\n1987     1860111   1953105    3813216\n1988     1909210   2004583    3913793\n1989     1973712   2071981    4045693\n1990     2030966   2131951    4162917\n1991     2011601   2103741    4115342\n1992     1985118   2084310    4069428\n1993     1953456   2051067    4004523\n1994     1932234   2024691    3956925\n1995     1904871   1998141    3903012\n1996     1902664   1992210    3894874\n1997     1896928   1987401    3884329\n1998     1927106   2018086    3945192\n1999     1934510   2028955    3963465\n2000     1984255   2079568    4063823\n2001     1970770   2060761    4031531\n2002     1966519   2060857    4027376\n2003     1999387   2096705    4096092\n2004     2010710   2108197    4118907\n2005     2022892   2122727    4145619\n2006     2084957   2188268    4273225\n2007     2111890   2212118    4324008\n2008     2077929   2177227    4255156\nAll     74035823  77738555  151774378",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>gender</th>\n      <th>F</th>\n      <th>M</th>\n      <th>All</th>\n    </tr>\n    <tr>\n      <th>year</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1969</th>\n      <td>1753634</td>\n      <td>1846572</td>\n      <td>3600206</td>\n    </tr>\n    <tr>\n      <th>1970</th>\n      <td>1819164</td>\n      <td>1918636</td>\n      <td>3737800</td>\n    </tr>\n    <tr>\n      <th>1971</th>\n      <td>1736774</td>\n      <td>1826774</td>\n      <td>3563548</td>\n    </tr>\n    <tr>\n      <th>1972</th>\n      <td>1592347</td>\n      <td>1673888</td>\n      <td>3266235</td>\n    </tr>\n    <tr>\n      <th>1973</th>\n      <td>1533102</td>\n      <td>1613023</td>\n      <td>3146125</td>\n    </tr>\n    <tr>\n      <th>1974</th>\n      <td>1543005</td>\n      <td>1627626</td>\n      <td>3170631</td>\n    </tr>\n    <tr>\n      <th>1975</th>\n      <td>1535546</td>\n      <td>1618010</td>\n      <td>3153556</td>\n    </tr>\n    <tr>\n      <th>1976</th>\n      <td>1547613</td>\n      <td>1628863</td>\n      <td>3176476</td>\n    </tr>\n    <tr>\n      <th>1977</th>\n      <td>1623363</td>\n      <td>1708796</td>\n      <td>3332159</td>\n    </tr>\n    <tr>\n      <th>1978</th>\n      <td>1626324</td>\n      <td>1711976</td>\n      <td>3338300</td>\n    </tr>\n    <tr>\n      <th>1979</th>\n      <td>1705837</td>\n      <td>1793958</td>\n      <td>3499795</td>\n    </tr>\n    <tr>\n      <th>1980</th>\n      <td>1762459</td>\n      <td>1855522</td>\n      <td>3617981</td>\n    </tr>\n    <tr>\n      <th>1981</th>\n      <td>1772037</td>\n      <td>1863478</td>\n      <td>3635515</td>\n    </tr>\n    <tr>\n      <th>1982</th>\n      <td>1797239</td>\n      <td>1888218</td>\n      <td>3685457</td>\n    </tr>\n    <tr>\n      <th>1983</th>\n      <td>1775299</td>\n      <td>1867522</td>\n      <td>3642821</td>\n    </tr>\n    <tr>\n      <th>1984</th>\n      <td>1791802</td>\n      <td>1881766</td>\n      <td>3673568</td>\n    </tr>\n    <tr>\n      <th>1985</th>\n      <td>1834774</td>\n      <td>1930290</td>\n      <td>3765064</td>\n    </tr>\n    <tr>\n      <th>1986</th>\n      <td>1833708</td>\n      <td>1926987</td>\n      <td>3760695</td>\n    </tr>\n    <tr>\n      <th>1987</th>\n      <td>1860111</td>\n      <td>1953105</td>\n      <td>3813216</td>\n    </tr>\n    <tr>\n      <th>1988</th>\n      <td>1909210</td>\n      <td>2004583</td>\n      <td>3913793</td>\n    </tr>\n    <tr>\n      <th>1989</th>\n      <td>1973712</td>\n      <td>2071981</td>\n      <td>4045693</td>\n    </tr>\n    <tr>\n      <th>1990</th>\n      <td>2030966</td>\n      <td>2131951</td>\n      <td>4162917</td>\n    </tr>\n    <tr>\n      <th>1991</th>\n      <td>2011601</td>\n      <td>2103741</td>\n      <td>4115342</td>\n    </tr>\n    <tr>\n      <th>1992</th>\n      <td>1985118</td>\n      <td>2084310</td>\n      <td>4069428</td>\n    </tr>\n    <tr>\n      <th>1993</th>\n      <td>1953456</td>\n      <td>2051067</td>\n      <td>4004523</td>\n    </tr>\n    <tr>\n      <th>1994</th>\n      <td>1932234</td>\n      <td>2024691</td>\n      <td>3956925</td>\n    </tr>\n    <tr>\n      <th>1995</th>\n      <td>1904871</td>\n      <td>1998141</td>\n      <td>3903012</td>\n    </tr>\n    <tr>\n      <th>1996</th>\n      <td>1902664</td>\n      <td>1992210</td>\n      <td>3894874</td>\n    </tr>\n    <tr>\n      <th>1997</th>\n      <td>1896928</td>\n      <td>1987401</td>\n      <td>3884329</td>\n    </tr>\n    <tr>\n      <th>1998</th>\n      <td>1927106</td>\n      <td>2018086</td>\n      <td>3945192</td>\n    </tr>\n    <tr>\n      <th>1999</th>\n      <td>1934510</td>\n      <td>2028955</td>\n      <td>3963465</td>\n    </tr>\n    <tr>\n      <th>2000</th>\n      <td>1984255</td>\n      <td>2079568</td>\n      <td>4063823</td>\n    </tr>\n    <tr>\n      <th>2001</th>\n      <td>1970770</td>\n      <td>2060761</td>\n      <td>4031531</td>\n    </tr>\n    <tr>\n      <th>2002</th>\n      <td>1966519</td>\n      <td>2060857</td>\n      <td>4027376</td>\n    </tr>\n    <tr>\n      <th>2003</th>\n      <td>1999387</td>\n      <td>2096705</td>\n      <td>4096092</td>\n    </tr>\n    <tr>\n      <th>2004</th>\n      <td>2010710</td>\n      <td>2108197</td>\n      <td>4118907</td>\n    </tr>\n    <tr>\n      <th>2005</th>\n      <td>2022892</td>\n      <td>2122727</td>\n      <td>4145619</td>\n    </tr>\n    <tr>\n      <th>2006</th>\n      <td>2084957</td>\n      <td>2188268</td>\n      <td>4273225</td>\n    </tr>\n    <tr>\n      <th>2007</th>\n      <td>2111890</td>\n      <td>2212118</td>\n      <td>4324008</td>\n    </tr>\n    <tr>\n      <th>2008</th>\n      <td>2077929</td>\n      <td>2177227</td>\n      <td>4255156</td>\n    </tr>\n    <tr>\n      <th>All</th>\n      <td>74035823</td>\n      <td>77738555</td>\n      <td>151774378</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 49
    }
   ],
   "source": [
    "births.pivot_table('births','year','gender',fill_value=0,aggfunc='sum',margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7ff887cadb10>"
     },
     "metadata": {},
     "execution_count": 84
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "births.pivot_table('births','year','gender',fill_value=0,aggfunc='sum').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}